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International Journal of Computer Assisted Radiology and Surgery

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25-02-2024 | Computed Tomography | Original Article

Automated personalization of biomechanical knee model

Authors: Alexandra Yurova, Alexey Lychagin, Eugene Kalinsky, Yuri Vassilevski, Mikhail Elizarov, Andrey Garkavi

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 5/2024

Abstract

Purpose

Patient-specific biomechanical models of the knee joint can effectively aid in understanding the reasons for pathologies and improve diagnostic methods and treatment procedures. For deeper research of knee diseases, the development of biomechanical models with appropriate configurations is essential. In this study, we mainly focus on the development of a personalized biomechanical model for the investigation of knee joint pathologies related to patellar motion using automated methods.

Methods

This study presents a biomechanical model created for patellar motion pathologies research and some techniques for automating the generation of the biomechanical model. To generate geometric models of bones, the U-Net neural network was adapted for 3D input datasets. The method uses the same neural network for segmentation of femur, tibia, patella and fibula. The total size of the train/validation (75/25%) dataset is 18,183 3D volumes of size \(512\times 512\times 4\) voxels. The configuration of the biomechanical knee model proposed in the paper includes six degrees of freedom for the tibiofemoral and patellofemoral joints, lateral and medial contact surfaces for femur and tibia, and ligaments, representing, among other things, the medial and lateral stabilizers of the knee cap. The development of the personalized biomechanical model was carried out using the OpenSim software system. The automated model generation was implemented using OpenSim Python scripting commands.

Results

The neural network for bones segmentation achieves mean DICE 0.9838. A biomechanical model for realistic simulation of patellar movement within the trochlear groove was proposed. Generation of personalized biomechanical models was automated.

Conclusions

In this paper, we have implemented a neural network for the segmentation of 3D CT scans of the knee joint to produce a biomechanical model for the study of knee cap motion pathologies. Most stages of the generation process have been automated and can be used to generate patient-specific models.

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Title: Automated personalization of biomechanical knee model
Authors: Alexandra Yurova
Alexey Lychagin
Eugene Kalinsky
Yuri Vassilevski
Mikhail Elizarov
Andrey Garkavi
Publication date: 25-02-2024
Publisher: Springer International Publishing
Keywords: Computed Tomography
Computed Tomography
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 5/2024
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-024-03075-5

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Automated personalization of biomechanical knee model

Abstract

Purpose

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusions

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